Tutorial and Examples.ipynb

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      "#*lifelines*: Survival Analysis in Python\n",
      "##### Tutorial & Examples\n",
      "\n",
      "\n",
      "*lifelines* gives a powerful tool to data analysts and statisticians looking for new methods to solve a common problem:\n",
      "\n",
      "> How do I measure durations?\n",
      "\n",
      "This question seems very vague and abstract, but thats only because we can be so general in this space. Some more specific questions *lifelines* will help you solve are:\n",
      "\n",
      "> How long can I expect a user to be on my service?\n",
      "> Which treatment group has a longer expected lifetime?\n",
      "> When is the next event likely to happen?\n",
      "> Which factors influence durations of events?\n",
      "\n",
      "The statistical tool that answers these question is Survival Analysis. *lifelines* is a implementation of survival analysis in Python. What benefits does *lifelines* offer over other survival analysis implementations:\n",
      "\n",
      "- built ontop of Pandas\n",
      "- internal plotting library\n",
      "- simple and intuitive classes and functions (*made for humans*)\n",
      "- only does surival analysis! (No unnecessary features or second-class implentations)\n",
      "\n",
      "\n",
      "Below is an example of what we can produce with *lifelines*:\n",
      "\n",
      "<img src=\"http://i.imgur.com/5cA3XBk.png\" width=800 />"
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      "#run me too!\n",
      "import numpy as np\n",
      "figsize(12.5,5)\n",
      "np.set_printoptions(precision=2,  suppress=True) "
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      "## Quick intro to Survival Analysis\n",
      "\n",
      "Traditionally, survival analysis was developed to measure lifetimes of individuals. An actuary or health professional would ask a question like \"how long does this population live for?\", for example the population may be a nation's population (for actuaries), or a population sticken by a disease (the the medical professional's case). Sorta morbid. \n",
      "\n",
      "The anaylsis can be further applied to not just traditional *births and deaths*, but any duration. Medical professional might be interested in the *time between childbirths*, where a birth in this case is newborn's birth, and a death is becoming pregnant again! (obviously, we are loose with our definitions of *birth and death*) Another example is users on a service: a birth is a user who joins the service, and a death is when the user leaves the service. \n",
      "\n",
      "What makes measuring durations difficult is the passage of time itself! At the time you want to make inference on durations, it is possible, likely infact, that not all the death events have occured. These individuals in our population who have not been subject to the death event are labeled as *right-censored*: all the information we have on these individuals are thier current lifetime (which is naturally *less* than their acutal lifetimes). A common mistakes data analysts make is simply choosing to ignore the right-censorted individuals. We'll shall see why next.\n",
      "\n",
      "\n",
      "Consider a case where the population is actually made up of two populations, $A$ and $B$. Population $A$ has a very low lifespan, say 2 time intervals on average, and population $B$ enjoys a much large lifespan, say 12 time intervals on average. Of course, we do not know this distinction before hand. Denote time $t$. At $t=10$, we wish to investigate the average lifespan. Below is an example of such a situation."
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     "input": [
      "figsize(12.5,6)\n",
      "\n",
      "from lifelines.plotting import plot_lifetimes\n",
      "from numpy.random import uniform, exponential\n",
      "\n",
      "N = 25\n",
      "current_time = 10\n",
      "actual_lifetimes = np.array([[exponential(12), exponential(2)][uniform()<0.5] for i in range(N)])\n",
      "observed_lifetimes = np.minimum(actual_lifetimes,current_time)\n",
      "observed= actual_lifetimes < current_time\n",
      "\n",
      "plt.xlim(0,25)\n",
      "plt.vlines(10,0,30,lw=2, linestyles=\"--\")\n",
      "plt.xlabel('time')\n",
      "plt.title('Births and deaths of our population, at $t=10$')\n",
      "plot_lifetimes(observed_lifetimes, censorship=observed)\n",
      "print \"Observed lifetimes at time %d:\\n\"%(current_time), observed_lifetimes"
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5ZTbEBwAAQIOTn5+v3/zmN7Yff8w13OFwWJMmTVKvXr0qNdvBYLBiu7M33nijyg9wSEtL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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x112960890>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Observed lifetimes at time 10:\n",
        "[  0.14   2.43  10.    10.     3.18   2.62   8.66   1.81   2.18   1.57\n",
        "   3.3   10.     2.69   0.69   0.09   0.86   0.46   0.59   5.18   4.9\n",
        "   1.02   2.74   0.32   0.12  10.  ]\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The red lines denote the lifespan of instances where the death has been observed, and the blue lines denote the lifespan of the right-censored instances (deaths have not been observed). If we are asked to estimate the average lifetime of our population, and we naivly decided to *not* included the right-censored individuals, it is clear that we would be serverly underestimating the true average lifespan. \n",
      "\n",
      "\n",
      "Furthermore, if we instead simply took the mean of *all* observed lifespans, including the current lifespans of right-censored instances, we would *still* be underestimating the true average lifespan. Below we plot the actual lifetimes of all instances (though we do not see this information at $t=10$)."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.xlim(0,25)\n",
      "plt.vlines(10,0,30,lw=2,linestyles=\"--\")\n",
      "plot_lifetimes(actual_lifetimes, censorship=observed)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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LWoFJNNwA0kY8Hlc0GlUikbAdBQAA12i44Ql8MhyACewtcItagUk03AAAAIBB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\n+p9l+jYAAACAJ/laW1tbTSw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       "text": [
        "<matplotlib.figure.Figure at 0x112d8b610>"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Survival analysis was originally developed to solve this type of problem, that is, to deal with estimation when our data is right-censored, and we can't solely rely on historical data either. (The particular case when all death events have been made is much simpler, and statistics like sample averages are as the usual form once again -- though survival analysis is still very useful in this case as we shall later).\n",
      "\n",
      "The observations need not always start at zero, either. This was done only for understanding in the above example. In the service example, where customers joining is a birth, a customer can enter observation at any time, and not necessarily at time 0.\n",
      "\n",
      "\n",
      "We next introduce the two fundamental objects in survival analysis, the *survival function* and the *hazard function*."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "-------\n",
      "\n",
      "Let $T$ be the (possibly infinite, but always positive) random duration taken from the population under observation.  The survival function, $S(t)$, of a population is defined as \n",
      "\n",
      "$$ S(t) = Pr( T > t) $$\n",
      "\n",
      "i.e., the probability the death event has not occured yet at time $t$. Note the following properties of the survival function:\n",
      "\n",
      "2. $0 \\le S(t) \\le 1$\n",
      "1. $S(0) = 1$\n",
      "2. $F_T(t) = 1 - S(t)$, where $F_T(t)$ is the cumulative density function of $T$, which implies \n",
      "4.  $f_T(t) = -S'(t) $\n",
      "\n",
      "We are also interested in the probability of dieing in the next instant, given we haven't expired yet. Mathematically, that is:\n",
      "\n",
      "$$ \\lim_{\\delta t \\rightarrow 0 } \\; Pr( t \\le T \\le t + \\delta t | T > t) $$\n",
      "\n",
      "This quantity goes to 0 as $\\delta t$ shrinks, so we divide this by the interval $\\delta t$ (sorta like we do in calculus). This defines the hazard function at time $t$, $\\lambda(t)$:\n",
      "\n",
      "$$\\lambda(t) =  \\lim_{\\delta t \\rightarrow 0 } \\; \\frac{Pr( t \\le T \\le t + \\delta t | T > t)}{\\delta t} $$\n",
      "\n",
      "It can be shown with quite elementary probability that this is equal to:\n",
      "\n",
      "$$\\lambda(t) = \\frac{-S'(t)}{S(t)}$$\n",
      "\n",
      "and solving this differential equation (yes, it is a differential equation), we get:\n",
      "\n",
      "$$S(t) = \\exp\\left( -\\int_0^t \\lambda(z) dz \\right)$$\n",
      "\n",
      "\n",
      "What I love about the above equation is that is defines **all** survival functions, and because the hazard function is arbitrary (i.e. it has no particular form), the entire function is non-parametric . Notice that we can now speak either about the survival function, $S(t)$, or the hazard function, $\\lambda(t)$, and we can convert back and forth quite easily too. It also gives us another, albeit not very useful, expression for $T$: Upon differentiation and some algebra, we recover:\n",
      "\n",
      "$$f_T(t) = \\lambda(t)\\exp\\left( \\int_0^t \\lambda(z) dz \\right)$$\n",
      "\n",
      "Of course, we do not observe the true survival curve of a population. We must use the observed data to estimate it. We also want to continue to be non-parametric, that is not assume anything more about the data. The *best*<sup>[1]</sup> method to recreate the survival function non-parametrically from the data is known as the Kaplan-Meier estimate. "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##### Example: Estimate $S(t)$ using the Kaplan-Meier estimate\n",
      "\n",
      "For this example, we will be investigating the lifetimes of political regimes around the world. A regime in this case is defined by a single individual's time in office who controls the regime. This could be an elected president, unelected dictator, monarch, etc. The birth event is the start of the regime, and the death event is the retirement of the individual.\n",
      "\n",
      "For example, the Bush regime began in 2000 and officially ended in 2008 upon his retirement, thus this regime's lifespan was 8 years and the \"death\" event was observed. On the other hand, the JFK regime lasted 2 years, from 1961 and 1963, and the regime's official death event *was not* observed -- JFK died before his official retirement. \n",
      "\n",
      "(This is an example that has gladly redefined the birth and death events, and infact completely flips the idea upside down by using deaths as the censorship event. This is also an example where the current time is not the impeidment for complete observation -- there are alternative events (eg: death in office) that can end a regime.) \n",
      "\n",
      "To estimate the survival function, we use the [Kaplan-Meier Estimate](http://en.wikipedia.org/wiki/Kaplan%E2%80%93Meier_estimator), defined:\n",
      "\n",
      "$$\\hat{S}(t) = \\prod_{t_i \\lt t} \\frac{n_i - d_i}{n_i}$$\n",
      "\n",
      "where $d_i$ are the number of death events at time $t$ and $n_i$ is the number of subjects at risk of death at time $t$. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "data = pd.read_csv('datasets/dd.csv', header = 0, index_col=0 )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data.head()\n",
      "#the boolean columns `observed` refers to whether the death (leaving office)\n",
      "#was observed or not."
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>ctryname</th>\n",
        "      <th>cowcode2</th>\n",
        "      <th>politycode</th>\n",
        "      <th>un_region_name</th>\n",
        "      <th>un_continent_name</th>\n",
        "      <th>ehead</th>\n",
        "      <th>leaderspellreg</th>\n",
        "      <th>democracy</th>\n",
        "      <th>regime</th>\n",
        "      <th>start_year</th>\n",
        "      <th>duration</th>\n",
        "      <th>observed</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> Afghanistan</td>\n",
        "      <td> 700</td>\n",
        "      <td> 700</td>\n",
        "      <td> Southern Asia</td>\n",
        "      <td> Asia</td>\n",
        "      <td>   Mohammad Zahir Shah</td>\n",
        "      <td> Mohammad Zahir Shah.Afghanistan.1946.1952.Mona...</td>\n",
        "      <td> Non-democracy</td>\n",
        "      <td>      Monarchy</td>\n",
        "      <td> 1946</td>\n",
        "      <td>  7</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> Afghanistan</td>\n",
        "      <td> 700</td>\n",
        "      <td> 700</td>\n",
        "      <td> Southern Asia</td>\n",
        "      <td> Asia</td>\n",
        "      <td> Sardar Mohammad Daoud</td>\n",
        "      <td> Sardar Mohammad Daoud.Afghanistan.1953.1962.Ci...</td>\n",
        "      <td> Non-democracy</td>\n",
        "      <td> Civilian Dict</td>\n",
        "      <td> 1953</td>\n",
        "      <td> 10</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> Afghanistan</td>\n",
        "      <td> 700</td>\n",
        "      <td> 700</td>\n",
        "      <td> Southern Asia</td>\n",
        "      <td> Asia</td>\n",
        "      <td>   Mohammad Zahir Shah</td>\n",
        "      <td> Mohammad Zahir Shah.Afghanistan.1963.1972.Mona...</td>\n",
        "      <td> Non-democracy</td>\n",
        "      <td>      Monarchy</td>\n",
        "      <td> 1963</td>\n",
        "      <td> 10</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> Afghanistan</td>\n",
        "      <td> 700</td>\n",
        "      <td> 700</td>\n",
        "      <td> Southern Asia</td>\n",
        "      <td> Asia</td>\n",
        "      <td> Sardar Mohammad Daoud</td>\n",
        "      <td> Sardar Mohammad Daoud.Afghanistan.1973.1977.Ci...</td>\n",
        "      <td> Non-democracy</td>\n",
        "      <td> Civilian Dict</td>\n",
        "      <td> 1973</td>\n",
        "      <td>  5</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td> Afghanistan</td>\n",
        "      <td> 700</td>\n",
        "      <td> 700</td>\n",
        "      <td> Southern Asia</td>\n",
        "      <td> Asia</td>\n",
        "      <td>   Nur Mohammad Taraki</td>\n",
        "      <td> Nur Mohammad Taraki.Afghanistan.1978.1978.Civi...</td>\n",
        "      <td> Non-democracy</td>\n",
        "      <td> Civilian Dict</td>\n",
        "      <td> 1978</td>\n",
        "      <td>  1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "      ctryname  cowcode2  politycode un_region_name un_continent_name  \\\n",
        "1  Afghanistan       700         700  Southern Asia              Asia   \n",
        "2  Afghanistan       700         700  Southern Asia              Asia   \n",
        "3  Afghanistan       700         700  Southern Asia              Asia   \n",
        "4  Afghanistan       700         700  Southern Asia              Asia   \n",
        "5  Afghanistan       700         700  Southern Asia              Asia   \n",
        "\n",
        "                   ehead                                     leaderspellreg  \\\n",
        "1    Mohammad Zahir Shah  Mohammad Zahir Shah.Afghanistan.1946.1952.Mona...   \n",
        "2  Sardar Mohammad Daoud  Sardar Mohammad Daoud.Afghanistan.1953.1962.Ci...   \n",
        "3    Mohammad Zahir Shah  Mohammad Zahir Shah.Afghanistan.1963.1972.Mona...   \n",
        "4  Sardar Mohammad Daoud  Sardar Mohammad Daoud.Afghanistan.1973.1977.Ci...   \n",
        "5    Nur Mohammad Taraki  Nur Mohammad Taraki.Afghanistan.1978.1978.Civi...   \n",
        "\n",
        "       democracy         regime  start_year  duration  observed  \n",
        "1  Non-democracy       Monarchy        1946         7         1  \n",
        "2  Non-democracy  Civilian Dict        1953        10         1  \n",
        "3  Non-democracy       Monarchy        1963        10         1  \n",
        "4  Non-democracy  Civilian Dict        1973         5         0  \n",
        "5  Non-democracy  Civilian Dict        1978         1         0  "
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "From the `1ifelines.estimation` library, we'll need the `KaplanMeierFitter` for this exercise: "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from lifelines.estimation import KaplanMeierFitter\n",
      "kmf = KaplanMeierFitter()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "For this estimation, we need the duration each leader was/has been in office, and whether or not they were observed to have left office (leaders who died in office or were in office in 2008, the latest date this data was record at, do not have observed death events)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "T = data[\"duration\"].values #measure in years\n",
      "C = data[\"observed\"].values "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Current, all arguments to *lifelines* estimators must be numpy arrays. We next use the `KaplanMeierFitter` method `fit` to fit the model to the data. (This is similar to, and was inspired by, another popular Python library [scikit-learn's](http://scikit-learn.org/stable/) fit/predict API)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "kmf.fit(T, censorship=C )\n",
      "kmf.fit?"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "After calling the `fit` method, the `KaplanMeierFitter` has a field called `survival_function_`. (Again, we follow the styling of scikit-learn, and append an underscore to all estimated properties) The property is actually a pandas dataframe, so we can call `plot` on it:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "kmf.survival_function_.plot()\n",
      "plt.title('Survival function of political regimes');"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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296N1Ye4OdpAXWEVWYAd5gSc12IBXVVXprrvu0qJFi7R582bNmTNHGRkZdc6Z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\nCcwAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x114040550>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "What do we see? The y-axis represents the probability a leader is still around after $t$ years, \n",
      "where $t$ years is on the x-axis. We see that very few leaders make it past 20 years in office. Of course, like all good stats, we need to report how uncertain we are about these point estimates, i.e. we need confidence intervals. They are computed on the call to `fit`, and are located under the `confidence_interval_` property. \n",
      "\n",
      "Alternativly, we can call `plot` on the `KaplanMeierFitter` itself to plot both the KM estimate and it's confidence intervals:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "kmf.plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x112ddb550>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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EGeQFRpGV9i8sLExFRUXuLgPtUFFRkcLCwlrs+ZpcAY+Ojm5wF6SsrKwGtyWV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\ngg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x113e1c210>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The median time in office, which defines the point in time where on average 1/2 of the population has expired, is a property:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "kmf.median_"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "KM-estimate    3\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Interesting that it is only 3 years. That means, around the world, when a leader is elected there is a 50% chance he or she will be gone in 3 years! \n",
      "\n",
      "Let's segment on democratic regimes vs non-democratic regimes. Calling `plot` on either the estimate itself or the fitter object will return an `axis` object, that can be used for plotting further estimates:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dem = data[\"democracy\"] == \"Democracy\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "kmf.fit(T[dem], censorship=C[dem], columns=[\"Democratic Regimes\"])\n",
      "ax = kmf.survival_function_.plot()\n",
      "kmf.fit(T[~dem], censorship=C[~dem], columns=[\"Non-democratic Regimes\"])\n",
      "kmf.survival_function_.plot(ax=ax, c=\"#A60628\")\n",
      "plt.ylim(0,1);\n",
      "plt.title(\"Lifespans of different global regimes\");"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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GKn12peMte1xPTw9BQUEIDQ2Fn58fmjVrhqlTp+LJkycAgFOnTqFTp06Qy+WY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\nAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1148eead0>"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We might be interested in estimating the probabilities inbetween some points. We can do that with the `timeline` argument. We specify the times we are interested in, and are returned a DataFrame with the probabilties of survival at those points:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "t = np.linspace(0,50,51)\n",
      "kmf.fit(T[dem], censorship=C[dem], timeline=t, columns=[\"Democratic Regimes\"])\n",
      "ax = kmf.survival_function_.plot()\n",
      "print \"Median survival time of democratic:\", kmf.median_\n",
      "\n",
      "kmf.fit(T[~dem], censorship=C[~dem], timeline=t, columns=[\"Non-democratic Regimes\"])\n",
      "kmf.survival_function_.plot(ax=ax, c=\"#A60628\")\n",
      "plt.ylim(0,1);\n",
      "plt.title(\"Lifespans of different global regimes\");\n",
      "print \"Median survival time of non-democratic:\", kmf.median_\n",
      "print\n",
      "print kmf.survival_function_.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Median survival time of democratic: Democratic Regimes    3\n",
        "dtype: float64\n",
        "Median survival time of non-democratic: Non-democratic Regimes    6\n",
        "dtype: float64\n",
        "\n",
        "   Non-democratic Regimes\n",
        "0                1.000000\n",
        "1                0.793046\n",
        "2                0.709215\n",
        "3                0.649822\n",
        "4                0.600676"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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GKn12peMte1xPTw9BQUEIDQ2Fn58fmjVrhqlTp+LJkycAgFOnTqFTp06Qy+WY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\nrkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x11403afd0>"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "It is incredible how much longer these non-democratic regimes exist for. A democratic regime does have a natural bias towards death though: both via elections and natural limits (the US imposes a strict 8 year limit). The median of a non-democractic is only about twice as large as a democratic regime, but the difference is really apparent in the tails: if you're a non-democratic leader, and you've made it past the 10 year mark, you probably have a long life ahead. Meanwhile, a democratic leader rarely makes it past 10 years, and then have a very short lifetime past that.\n",
      "\n",
      "\n",
      "Here the difference between survival functions is very obvious, and performing a statistical test seems pendantic. If the curves are more similar, or we possess less data, we may be interested in performing a statistical test. In this case, *lifelines* contains routines in `lifelines.statistics` to compare two survival curves. Below we demonstrate this routine. The function `logrank_test` is a common statistical test in survival analysis that compares two event series' generators. If the value returned exceeds some prespecified value, then we rule that the series have different generators. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from lifelines.statistics import logrank_test\n",
      "\n",
      "logrank_test(T[dem], T[~dem], C[dem], C[~dem] )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "602.63078762724956"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "-------- \n",
      "\n",
      "### Getting data into the right format\n",
      "\n",
      "*lifelines* data format is consitent across all functions: arrays of individual durations, and the individuals censorship (if any). These are often denoted `T` and `C` respectively. For example:\n",
      "\n",
      "    T = [0,3,3,2,1,2]\n",
      "    C = [1,1,0,0,1,1]\n",
      "    kmf.fit(T, censorship=C )\n",
      "\n",
      "The raw data is not always available in this form, so *lifelines* includes some helper functions for transform common data formats to *lifelines* format. These are located in the `lifelines.utils` sublibrary. For example, the function `datetime_to_durations` accepts an interatable of start times/dates, and a interatable of end times/dates (or `None` if not observed):"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from lifelines.utils import datetimes_to_durations\n",
      "\n",
      "start_date = ['2013-10-10 0:00:00', '2013-10-09', '2013-10-10']\n",
      "end_date = ['2013-10-13', '2013-10-10', None]\n",
      "T,C = datetimes_to_durations(start_date, end_date, fill_date='2013-10-15')\n",
      "print 'T (durations): ', T\n",
      "print 'C (censorship): ',C"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "T (durations):  [ 3.  1.  5.]\n",
        "C (censorship):  [ True  True False]\n"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The function `datetimes_to_durations` is very flexible, and has many keywords to tinker with."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##### Estimating hazard rates\n",
      "\n",
      "The survival curve is a great way to summarize and visualize the lifetime data, it is not the only way. We showed the relationship earlier between the survival function, $S(t)$, and the hazard function, $\\lambda(t)$, repeated here:\n",
      " \n",
      "$$S(t) = \\exp\\left( -\\int_0^t \\lambda(z) dz \\right) $$\n",
      "\n",
      "Often we denote the integral simpler:\n",
      "    \n",
      "$$ \\Lambda(t) =  \\int_0^t \\lambda(z) \\;dz$$ \n",
      "\n",
      "\n",
      "We call $\\Lambda(t)$ the cumulative hazard function. From above, the following relationship should be understood:\n",
      "\n",
      "$$ \\frac{d \\Lambda(t)}{dt} = \\lambda(t)$$  \n",
      "\n",
      "\n",
      "If we are curious about the hazard function of a population, we unfortunatly cannot use a transformation of the Kaplan Meier estimate -- statistics doesn't work quite that well. Fortunately, there is a proper estimator of the *cumulative* hazard function, called the Nelson-Aalen estimator:\n",
      "\n",
      "\n",
      "$$\\hat{\\Lambda}(t) = \\sum_{t_i \\le t} \\frac{d_i}{n_i} $$ \n",
      "\n",
      "where $d_i$ is the number of deaths at time $t_i$ and $n_i$ is the number of susceptible individuals. \n",
      "\n",
      "In *lifelines*, this estimator is available as `NelsonAalenFitter` in `lifelines.estimation`. Let's use the regime dataset from above:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "T = data[\"duration\"].values \n",
      "C = data[\"observed\"].values "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from lifelines.estimation import NelsonAalenFitter\n",
      "\n",
      "naf = NelsonAalenFitter()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "naf.fit(T,censorship=C)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 22
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "After fitting, the class exposes the property `cumulative_hazard_` as a DataFrame:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print naf.cumulative_hazard_.head()\n",
      "naf.plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   NA-estimate\n",
        "0     0.000000\n",
        "1     0.337384\n",
        "2     0.525407\n",
        "3     0.697290\n",
        "4     0.904979\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 23,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x11495b7d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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GALS0tGD+/PkYOnQohg0bhr1793ojLiIiIiIKcFqjBQAQFyQz1j2uY33HHXfg\nyiuvxF133QWz2QyNRoOoqCj7ea5jTURERERdMVqsEAGQhgReo4S761hLujvZ2tqKXbt24W9/+1vH\nYInEoagmIiIiIupOaAAW1L3V7SstKytDfHw87rzzThQVFeHuu++GVqv1VmzUD7G3jYRirpA7mC8k\nFHOFPKnbwtpsNqOkpARLly5FSUkJlEolVq5c6a3YiIiIiIgCRretIKmpqUhNTcXll18OAJg/f77L\nwnrp0qVIT08HAERFRSE/P9++TmTnJ0Me87hTcXGx38TDY/89njBhgl/Fw2P/Pma+8JjHPO6L486f\nKyoqAACLFy+GO3q8eHHSpEl49913MWjQIDz77LPQ6XRYtWqV/TwvXiQiIiKii2nOrwiiDA3xcSS9\n5+7Fiz12k7/xxhu49dZbUVBQgEOHDmHFihWXFCAFtws/ERJ1h7lC7mC+kFDMFe/519EGzF17CO9+\nf9bXoXiNpKcBBQUF2LdvnzdiISIiIqJ+ol7TsetiTFhwbA4DcOdF8rLOXiainjBXyB3MFxKKueI9\n9UG26yLAwpqIiIiIPMC+nXl4cOy6CLCwJi9jbxsJxVwhdzBfSCjmivd0toLEccaaiIiIiKh3LFYb\n5BIxZCEixAZRj3WPy+31hMvtEREREZErVpsNYpHI12H0Wp8vt0dERERE1BuBXFT3Bgtr8ir2tpFQ\nzBVyB/OFhGKukCexsCYiIiIi6gPssSYiIiIicoE91kRERETkU9VqAxq1Jlgvbf424LCwJq9ibxsJ\nxVwhdzBfSCjmine8uL0ct/z9MI6e0/g6FK9iYU1EREREfapzc5h4ZfDsugiwsCYvmzBhgq9DoADB\nXCF3MF9IKOaK55mtNjRrzRABiFUEz+YwAAtrIiIiIupDTVoTbOgoqiVirmNN5DHsbSOhmCvkDuYL\nCcVc8bz69o42kDhlcM1WAyysiYiIiKgPma02ZETLkRYt93UoXsd1rImIiIiIXOA61kREREREPsDC\nmryKvW0kFHOF3MF8IaGYK+RJLKyJiIiIiPoAe6yJiIiIiFxgjzURERER+YTFasOhmnbUtBl8HYpP\nsLAmr2JvGwnFXCF3MF9IKOaKZzXpTHjsXyfx8D9O+DoUn2BhTURERER9or7dBACIU4b6OBLfYGFN\nXjVhwgRfh0ABgrlC7mC+kFDMFc9q0ATvrosAC2siIiIi6iP1mo4Z63jOWBN5HnvbSCjmCrmD+UJC\nMVc8q/4D8ljhAAAgAElEQVT8jHU8Z6yJiIiIiHovViHFoDgFBkTJfB2KT3AdayIiIiIiF7iONRER\nERGRD7CwJq9ibxsJxVwhdzBfSCjmCnkSC2siIiIioj7AHmsiIiIiIhfc7bGWCBmUmZmJyMhIhISE\nQCqV4vvvv+91gERERETU/9S0GVDVYkBatAxJEcG5KoigVhCRSIRvvvkGBw4cYFFNl4S9bSQUc4Xc\nwXwhoZgrnrP3TCue+ncpNvx0zteh+IzgHutL7BghIiIion6sc9fFYN3OHHBjxnratGkYNWoU3nnn\nHU/HRP3YhAkTfB0CBQjmCrmD+UJCMVc8p3PXxThFcG5nDgjssf7222+RnJyM+vp6XH311RgyZAgm\nTpzo6diIiIiIKEA0nJ+xTggP3hlrQYV1cnIyACA+Ph5z587F999/71BYL126FOnp6QCAqKgo5Ofn\n2z8RdvYy8ZjHAPD2228zP3gs6PjCPkh/iIfH/n3MfOGx0OPO2/wlnv50fOxAGZAyHHHKUL+IpzfH\nnT9XVFQAABYvXgx39LjcnlarhcViQUREBDQaDaZPn45nnnkG06dPB8Dl9sg9xcXF9iQm6g5zhdzB\nfCGhmCue89KOM6hVG7By5kCESvrHVinuLrfXY2FdVlaGuXPnAgDMZjNuvfVW/O53v7OfZ2FNRERE\nRP1Rn69jnZWVhYMHD15SUERERERE/V3/mKengHFhDxNRd5gr5A7mCwnFXCFPYmFNRERERNQHeuyx\n7gl7rImIiIioP+rzHmsiIiIiou7sq1TDYLYiPzkcUfLgLS/ZCkJexd42Eoq5Qu5gvpBQzBXP+PDH\nWvzntjKUN+l8HYpPsbAmIiIioktS396x62KcMni3MwdYWJOXcVF+Eoq5Qu5gvpBQzJW+Z7XZ0Kjt\nLKyDdztzgIU1EREREV2CVp0ZZqsNkbIQyPrJjou9FdyvnryOvW0kFHOF3MF8IaGYK32vXtMxWx0f\nHtxtIABXBSEiIiKiSyCXijFzsAoqRc9tIGq9GdtLmwEAV+XEILKfrSDCdayJiIiIyOPUejPu++TY\nLzPcSinenjvEr4trd9exZisIEREREXnc9tJme1ENdLSQdM5e9xcsrMmr2NtGQjFXyB3MFxKKuUKe\nxMKaiIiIiDzuqpwYKKS/lJ7xSimuyonxYUR9z3+bWqhf4vqhJBRzhdzBfCGhmCu+EyELQbRcAq3J\niOuGxuGOy5L9ur+6NzhjTURERES9YrXZsP7HOmw71YSe1sMob9ajus2ISFkIlo5N7XdFNcDCmryM\nvW0kFHOF3MF8IaGYK32rVW/GX/dV4897qiASibodu6usBQAwPjMaIeLuxwYqFtZERERE1CsNFyyd\n1x2bzYYdpztWAJmUFe3xuHyFhTV5FXvbSCjmCrmD+UJCMVf6Vr3GCACIU3a/62J5sx6VrQZEykJQ\nmBLhjdB8goU1EREREfVK54x1XA8z1sHQBgKwsCYvY28bCcVcIXcwX0go5krf+mUXxa5nrIOlDQTg\ncntERERE1EvDE5XQD4vHsERll2OCpQ0EYGFNXsbeNhKKuULuYL6QUMyVvnVFehSuSI/qdkywtIEA\nbAUhIiIiIg8JpjYQgIU1eRl720go5gq5g/lCQjFXvCuY2kAAFtZERERE5CHB1AYCsLAmL2NvGwnF\nXCF3MF9IKOaK9wRbGwjAwpqIiIiIeqG0UYu1P9RgX6Xa5flgawMBWFiTl7G3jYRirpA7mC8kFHOl\n7xyp0+D9A7UoLm9xeT7Y2kAAFtZERERE1AsN9s1hnHddDMY2EICFNXkZe9tIKOYKuYP5QkIxV/pO\nvcYIAIhzsetiMLaBANwghoiIqN+pbNFjdXElGjRGJEfKsHLmQKcxZ1sNWPHFKafbOZ7jhY7vbPWI\nczFjHYxtIAALa/Ky4uJizhaQIMwVcgfz5RenG3V48vNTaNGbAQCSLooai9WGmjaj0+39fby69CAi\ncwr9Jp5AHm+w2BAmFSNHFeZwLljbQABAZLPZbD0NslgsGDVqFFJTU7FlyxaHc9u2bUNRUZHHAqT+\nhf/4kVDMFXIH86XDiXotfvfFKbQZLCgaEIH7x6UiNESMhHDnr+pNFivqz/fIXkgiFvXr8d/v2Y3R\nY8f5TTyBPj5KLoEyNMThXFmTDvduOoZIWQjW35of0DPWJSUlmDp1quDxgmas//jHP2LYsGFoa2vr\ndWBEAHvbSDjmCrmD+dKhtEmHNoMFY9Oj8NSUTIRKur6UShoiRkqkTPBj95fxN8y4yq/i6Y/jg7UN\nBBBw8WJVVRU+++wzLF68GAImt4mIiMhHZg5W4T+nZ+PpaVndFtVEnhLMbSCAgML64Ycfxssvvwyx\nmP+D0qXj+qEkFHOF3MF8+cWY9Kgu+2KJueJpwboaSKduW0H++c9/IiEhASNHjsQ333zT5bilS5ci\nPT0dABAVFYX8/Hz713KdCcxjHgPATz/95Ffx8JjHPOYxj4PruJO/xNPfjk+H5QAABrSdxJ7dap/H\n05v8KC4uRkVFBQBg8eLFcEe3Fy+uWLEC7733HiQSCfR6PdRqNW688UasXbvWPoYXLxIREXnf1pNN\nyIyRY2CcwtehEAHoaANZvOEoKlsNePGaHFyWGunrkC6Zuxcvdtvf8cILL6CyshJlZWVYt24dpkyZ\n4lBUExERkff940g9XtpxBr/7ohSt55fVI/K1YG8DAdzceVEkYs8WXZqLv4oj6gpzhdwRTPny8aE6\nvLm7CgBw84gERMklPo4osARTrnhbMK8G0knw/41XXnklrrzySk/GQkRERF2w2Wx4r6QW7x+oBQA8\nOD4Ns4fG+Tgqog7BvhpIJ37MJa/qvEiAqCfMFXJHMOTLyUYdPjhQC7EIeHRSOq7OVfk6pIAUDLni\nC2wD6cDCmoiIKAAMilPgwQlpiAgNwaTsGF+HQ+SAbSAdWFiTVxUXc9thEoa5Qu7w93wxWqwIDXF9\nWdPmn+vRpHXeSnrOsHiolFKH22YNYevHpfL3XAlEbAP5BQtrIiIiD2kzmLH553r840gDVl+XiwFR\ncqcx/z7RiNJGndPtk7KinQprIn/ENpBfsLAmr+IsAQnFXCF3+Fu+tOhM2Hi4HluO1ENrsgIAdpa1\n4JbCJKex1w+LR4vOecY6VsGi2hP8LVf6A7aB/IKFNRERUR/6trwFK785A4O5o6AuGhCBRYVJGJEc\n7nL8zMG8CJECF9tAHLm1jjXRpeL6oSQUc4Xc4U/5khungNVqwxVpkfjj9YOwcubALotq8j5/ypX+\ngG0gjjhjTURE1IcSwkPxt5uHIU4Z6utQiDyObSCOOGNNXsXeNhKKuULu8Ha+VDTrseqbchw9p3F5\nnkW1/+J7S99hG4gzzlgTEREJVNtmwF/3VWPn6RbYAGiNVvxheravw3Ki1puxvbSj4LkqJwaRArc9\n7+39esubcQbKawuk59vw0zlUthoQHipmG8h5IpvNZruUB9i2bRuKior6Kh7q57h+KAnFXCF3eDpf\nDGYrPjpUh/U/1sFosUEqFmHGIBUWFCQgKULmseftDbXejPs+OYZ6TcdKI+GhIZg/IgFySfdfUutN\nFmz4qR7tRotb9+ut3j5fb+534X3UpQeRMvQyv3xtgfp8cokY7y/M83gx7wslJSWYOnWq4PH97zdA\nRETUx7QmCzYdrofRYsPk7GgsHj0ACeH+2e7xyc/n7EU1ALQbLfi//TVuP05v79db3owzUF5boDyf\n3mzF9tJmzMmL99pz+isW1uRVnIEkoZgr5A5P50tMmBQPjk+DSiHBiGT//MrbZrPhX8casf7Hc07n\nRiSFI0cV1u39Sxt1OFTb7vb9equ3z9eb+114n8icQsHP1VuB8rvsy+ejDiysiYiIBLgqJ8bXIXSp\nSWvCa7sq8H2lGgAgk4hgMHd0esYrpfj9tKwev6a/uIVE6P16q7fP15v7BcprC+Tn8+f/P7yJPdbk\nVeybJaGYK+SOvsgXq82Gbaea8G15K34/LQtiUWAsHVZc1oLVxRVQGywIDw3Bg+PTUDQgghcvdnGf\nYwe+w33zr/Hb18bn8y/ssSYiInLTiQYt/ry7CkfOL5+3t6IV4zL8e/kwjdGCt/dU4cuTTQA6dnh8\nbFK6fam/3vS7RsolXu2T7e3z9eZ+nfdRNUd7pQgMlN9loDxfoGBhTV7FGUgSirlC7uhtvrTqzfif\nfdX44ngjbABiwiRYPDoFY9Kj+jbAPnaoph0v7ziDunYjQkNEuHv0AFw3LC5gZtl9ie8t5EksrImI\nKGjtKmvB58cbESIC5g5PwK0jk6AMDfF1WF0yWqz42/4abPjpHGwAcuPC8MTkTKRHy30dGhGBhTV5\nGftmSSjmCrnDVb7oTBaUN+tR3qRDVasBi0enQHTRjO7MwSqUNekwZ1g80mM8V5z2RS9xdmwY3tpT\nidNNeohFwKLCJNw6MgkSbiPtFr63kCexsCYion7DZrPhha/LcbJRi2q10eHcvPwEqBRSh9tCxCI8\nMD7NozFdvILCR4fq8PbcIW6vftEpJVKGJyZnYGiC0mMxE1HvsLAmr+IsAQnFXKHuWKw2iESw9xR3\n5otIJEJFix7VaiMkYhHSo2XIjAlDVmyYz2Z2vzrZ5FAc12tM+O0/jtsvMuxKg8boVFTnJSrwwjUD\nESb133YVf8f3FvIkFtZERBQQzFYbDla3YVdZC74tb8Hvp2VjRHK407gHJ6RBIQ1BapQM0hDPbOnc\nE5vNhiPnNNh2shlfnWp0On9WbcTZi2bUhZicHcuimsiPsbAmr2JvGwnFXKFOx85p8K9jDdh9phVt\nBov99oPVbfbC+sJ8yUt0Lra9pVptwNaTTfi6tMmhFSVELILF2rFtRJRcgocmpEHRw0WSGqMFfyyu\nRKveDICbcPQVvreQJ7GwJiIiv3aiQYt/n+hYqzkjWo6JWdGYlB2NzBjPbNfcHVcXIar1Zuwsa8HW\nk032dbABQKWQYkpODKblxkKlkPbq4sURSeFBsQkHUX/BnReJiMjnNEYLzrYaMChe4XSuSWvCP482\n+KyY7nTxxYSRshAMiVfgQHU7TOdno+USMSZkRmFabiwKkiMQwhU7iAIad14kIiK/Z7JYcaxeiwNn\n21Bytg3H6jVQhobgo1vznYrRWIUUv7os2UeR/mJ7abPDxYRqgwXfV7VBLAIuGxCBqQNjMT4zij3Q\nREGMhTV5FXvbSCjmSv9lttpwy98PQ31Bv7RYBKRHy6HWmxFz0ZJ4QngyX2w2G36u0+CzYw1O58Zl\nROGBcWlQKd2PmXyD7y3kSSysiYjII+o1RkTIJJBLHFfmkIhFyIoNQ4vejKKUCIwcEIH8pHC/2/HQ\nYrXh2/IWbPjpHI7Va53OxyuleGRiOvueiciOPdZERHTJTjZocbxei3PtRpxrN+JEgxZVrQb8floW\nJmRGO403mq0IlfhmKbye6EwWfHG8EZsO16OuvWNljwhZCK4bGocpOTE4UN0OgBcTEgUD9lgTEVGf\nsFhtqD+/SUlnwZyXGO5y7ehtp5qw6XC9w20KqRgtOrPLx/bHorpBY8SnP9fjX8ca0W7saFNJiZRh\n3vB4XJ37y/rR6T68gFKo0kYtthxtgFrf8TrkUjGWX5nhNE5nsuDlHRVOt3M8xwsd/z/7qjE/PxGJ\nEd1veBQsWFiTV7G3jYRirvjWhp/OYe0PNdCbrQ63LyxIdFlYD08Kh85kRbxSioTwUKRGyTE4XuG1\nVTHczZcLl83LignDFycb8U1pM8znV/fIS1Rifn4CxqRHBdTKHu0GM1Z9cwbfVaodbg/vos3GbLWh\nuLzF6fb+PP6zbd8gMqfQb+IJ9PGfHmnAZ8cbMS8vHgsLk/yupcvbWFgTEQWhJq0Jx+u1CJOKUZgS\n4XQ+UhYCvdmKWIUESeEyxIdLkaAMdVlUA8CEzGiXLR/+qEZtwENbTqD5otl0sQiYmBWN+fkJGJqg\n9FF0l0YZGoJ6jQmyEBGuHRqHvMSO19HVdu5yiRj/MTXT6fb+PP72oiTkj8oUPN7f4ven8aEhYlyV\nE4Ptpc1Yf+gcvjjRhNuLkjBrSFxAfSDtSz32WOv1elx55ZUwGAwwGo2YM2cOXnzxRft59lgTEfm/\nc+1GfF3ahOPntDjeoEXD+WXjRqdF4r9m5DiN1xgtMFqsiAnz79UuXG3YYrPZ0KQ1o7rNgBq1AdVq\nA2rajKg5/3fnToYXGpEUjkcnpSM5Uubtl9DnShu1UCmkiPbz/3bUfxw7p8F/f3cWh+s6Nkj69WXJ\nWDQyycdR9Y0+77GWy+XYvn07FAoFzGYzJkyYwK9oiYj8kNVmQ4vOjFgXy9U1ak34n3019mOFVIzc\nOIV9RvNiytAQKOHfX+mq9Wbcs+komrQdhfI7359FQngo6tuNMFi6njOSiADzRacnZkUHTFFttdnw\nbXkrtCYLZgxSOZ3PUTlvskPkSUMSlHh1di6+LW/F+kN1mD00ztch+YygVhCFouN/UqPRCIvFgtjY\nWI8GRf0XP5SRUMyV7pmtNpyo1+J0kw6nG3UobdKirEmPKLkE7y3McxqfExuGOcPiMChegcFxSqRG\nyyAWBd5XtQ0aIw5Wt+PHmjbsLm9F2/mLDNWlBxGZU4iqVgMAIEouQXJEKJIjZUiOCEVKpAzJkTKk\nRMggEQNLNx+3b/YSr5TiqpwYn70moSxWG7aXNmPdj3WoaNEjQhaCSVnR3JDGTXxv8QyRSIQJWdGY\nkBUYLWGeIqiwtlqtKCoqQmlpKe677z4MGzbM03EREQUtncmCVr0ZLTozWvVmXJ4W6VQEG81WPLTl\nhNN9w602GMxWyC5adSNUIsaycWkejftSuGrpAIBmnQmHatpxsLoNP9a02wvnriwYkYBbBFxA9fbc\nIS6fzx/ZbDZ8frwR63+sQ01bx/J/CeFSLBiRGLR9rBR4DtW04WB1O24akdCvPwwKeicRi8U4ePAg\nWltbMWPGDHzzzTeYPHmyh0Oj/oizBCRUf8wVg9mKarUBmTFyiFzMFt+98Shq24wwXLQSx4bb8p0K\nP0VoCK5Ii0SEXILs2DDkxIYhWxWGKD8uELui1ptx3yfH7DPI75XUYHxmNI6c0+BMs95hrEIqRn5S\nOAqSwzEwToGXd5xBvcaEyJxCxCs7ik0hqxJEyiWYkxfvkddzIavN5vKbgbU/1GDT4XNOt99WlIz5\n+QkOt4lEIuwsa0FNmxEDImVYWJiIqQNju7ygjLrXH99b/J3NZsNf9p7FqUYdPjvWgDsuS8b0Qap+\n+cHQrXfgqKgozJo1C/v373corJcuXYr09HT7mPz8fHviFhcXAwCPecxjHgfd8St//xdq1QZIMkbg\nbKsBp378HjYA/3r6NqgUUqfxZ4/8gCatCapBIxEtl0BffgjhshCYrMNdPv4MZUfP9IR8/3i97h7v\n3LkLte0GnJLnoF5jgrr0IAAAOYX4/Hgj1KUHESoWYdyECShMCYel4jDSomSYNKnA/ni/SrRAn9jx\nLaq87ggO7d/r09enNVmgyCrAweo2fPn1DlyWGolnfn2903iTxYraYyUAYF/6TV16EEegAvKdx//6\nsmQMaDuJgqRwTBo0zC/++/GYx0KPRSIRxoZUoqGxAU2qIXi9uBKH9u/F+Mxov4jvwuPOnysqOtbz\nXrx4MdzR46ogDQ0NkEgkiI6Ohk6nw4wZM/DMM8/Yr5DkqiDkjuJi9raRMP6eK2arDWeadSht1GFM\nepTLVoJ7Nx5F2QUzriEiIDlShqenZiEr1nmTkQaNEQppCMKkYpcz2oGgq5YOm82GmjYjjtdrcLy+\nY5fGUw3aLi8yHJ0agZsLkzA4XoHQkJ43k/F1vvxY3YZ3vq/GqUYtrBe8pK5WXTGarTBZnV+7NEQk\n6PVS7/k6V4KZ1WbDjtMt+MeRerxwTU5AtIT0+aogNTU1uOOOO2C1WmG1WnH77be79QRERP3F3opW\nHDjb1lEUNmphPF8UrpyZg6IBkU7jrxsWD73JgtRoOVKjZEiKkHX79X2c0n92LuuqQO7pPhe2dLx/\noAbTc1Uoa9bheL0WbQaL031SImXIjpXjYHW7fbfDeKUUyydn+nXf88VkEjFONGghEYswLFGBkSkR\nKEqJwOAu1sMOlYjhP/+1ibxDLBLhqpyYgLhYuLd6nLHuCWesiai/sVhtLnv/nv+6DDtO/7IrWUqk\nDLlxYZifn4DB8YG5oYgrFxfI8Uop3p47BOGyELQZLGjSmtCkNaFZZ0aTzoRmrQlNOjNONGhwttXY\n5eNGyyUYHK/A4AQlhsQrMChOYS+ee1PIe4vNZsPJBh12ljWjokWP/5zuPANtsdpQcrYNw5OUATEL\nR0TC9PmMNRFRf9ZZNP1wVo1j9VqcqNfi5oJE3ODiwrarcmKQFROGQRcVhf1Jm8GMDw7U2otqAKjX\nmHDH+p+hN1vRzfLQLo1MDsesYXEYEq9EvFLaZYuLty4mFMpms+Fkow47TzdjZ1kLatt++cBQ2aJH\nWrTcYXyIWITL05y/tSCi4NL//lUgv8beNhLKG7my43Qz1uw9iwatyeH20406l+PHZURjXIZHQ+pz\nXc0Ea4wWVLToUd6kQ3mLHmeaO/40XvS76KQxdaxUEiELQUyYFDFhEsQqzv8dJkWMQgJZiBhv7amy\nbxUer5TiqalZXvkA4ol8Wbm93L68X6xCgomZMZiUHY0BUYGxkQy5xn+HyJNYWBNR0AoPDUGD1oQ4\nhRRXpEciLzEcg+MV/aZwUuvNWLLpmP2Dw//sr8aQeCWqWvUOM9IXkoWIMCBKhrPqX5b9iwmT4MVr\ncpAaLe/xwrrClAi/belwxWazwWS1Ob0ukUiE2UPjUKM2YGJWDPISlf1yaTAi6lvssSaifquuzYjd\nZzq+xr9vbKrTeZPFirImPXLjwvx+FY7uepDNVhtq1AZUtRpQ2apHVYsBVa16lDbqoLtoTexO0hAR\n0qPlyIiWIyNGjsyYMGTGyJEYEQqxSOTXPc+91ao342SDFk1aExq1JtS3m1BSrcaUnFj86rJkX4dH\nRH6IPdZEFNRONmix50wrdp9pxemmjpYOEYCFhYmICZM6jJWGiDEoXuHV+HpTsDZrTVi2+bh95nlt\nSQ2uyo5BXbsRZ9UG1KgNgnufrxmswoIRCUiOkHU7A+tvPc9dsdlsqG03olZtROP5gjkmTILpg1RO\nYw/XtuMPW8ucbj9U0+6NUIkoCLCwJq9ibxsJ1dtcef7rMlSrOy40C5OKMSo1EmPToyCX+H5tYFfL\n0d02MgkmK9BuMKPNYEGb/e9ffu5chq5Tm8GCfxxtsB+LACSGhyI1SobUKDnSomVIjZIhSi7B7788\n7bC6x+LLU/rF7DPQsXb0s1vLoDFaoC49aN9oZXii0mVhnRIpw8iUcKgUUqgUUsQqpMhRhSEvMdzb\noZMP8d8h8qT+8e5KRP2exWpDebMOR89pcfScBvOGxyNH5TzbPC4jGjqTBWMzolCYHIFQDxXUXc08\nW6w2NGpNqG0zoq7dgLo24/mfjTjdqEPbBUVyq96Ct/ac7dXzX5EWiatzY5EaJceAKBlkXbzOt+cO\nCciWDp3JgtNNHRvw6ExW3FyQ6DQmPjwUGqMF0XIJ4mLDkJ8TA5VCiowYuYtHBLJiw7Dq2lxPh05E\nQYw91kTk17462YgvTzTheL0W+gv6hZeMGYB5wxN8ElOz1oT7PjmGpvOrX8glYuTGhaFBY8K5dqNb\nS9INjgvDsMRwRMhCEC6TIEIWcv6PBJHn/7ZYbbj/0+NO60oHSpEslMZoweriCpQ26nC21YDOX6NC\nKsamX42A+KI+eJvNhiatGbEKid/3yBNRYGKPNRH5NYPZigbNL/2wDZqOv/MSwzExK9pp/Ll2E348\n3wObHBGKoQlKDE1QYlRqRJ/E01PPc7POhPImPU436VDerENZkx6nG7UwX1A8681W/FSrsR/HhEmQ\nFBGKxPBQJEbI7D9HhIrxn9vKHQrk568ZKKhADtSZ505GixVnWw2oaNGjqtWARYWJTsWwXCLGnjOt\nMFpsCBEBGTFhGKgKQ44qDGarDaEhjuNFIhFUSse+eSIiXwqsd2YKeOxtCw4Wqw16sxXKUOcd6D47\n1oC39zq3P+jNVofCujNXJmdHIzs2DEMSFE4XH16qi3ue/36wFgsLElHbbkR5U0cR3aI3C3qs2UNU\nuGF4AhLCQ7vt5+5tgRwoFxNebOX2cpxo0KJabYD1gg8jV+fGIiHccVPvELEIT16VicTwUGTE9Ly0\n34X43kJCMVfIk1hYE9El05utOFGvweFaDQ7XteNInQYTs6Lx6CTn3VQSwkORFBEKlUKKOIUUscqO\nC8kGx7lenWNAlBwDolz3zF4Kg9mKtSU1Dus5N+vMTkW/QiruWIouVo6smDBkxYZBpZBg+WenHGae\nfz1K2EWBgVogd/rwYC3Km/VoM5ih1lugNpih1pvx2uxByFaFOY3vnKEWizouHsyIliM9Ro6QLlo3\nJmQ6f2tBRBQoWFiTV3GWoP85WN2GFV+Uwmx1bCy+cAvoC43PjMZ4AcVTX+eKxWrDyQYtDlS34UB1\nG36u08Dkohl6oCoMEzKjkRUbhqxYORLDQ1327wZ6a0anUw1anGnRo0ZtQHWbEbVqA6rbDPiv6TkY\n6OLDzveVavxcp3G6XW1wPbN//7g0yCVipEbJPHYhKcD3FhKOuUKeFJj/EhCRV9VrOla0uCI9yulc\nRowcVpsNOaowDE9UYlhiOPISlU5f83vaxb3SEbIQVLYY7IX0jzXt0Fy0bF1mjBx17Ubozm/XHa+U\nYuVMYT3PgTDzbDBbUdduRF2bETmqMMQqnFtp3t1XjZKzbU63V6sNLgvrBSMSoTFaECnvvMBSgkh5\niMu2HwAYlqi89BdCRBQgWFiTV7G3LTAYLVb8XKvBvio19lepUd6shzREhE9+NcKp7zUmTIpNt4+A\noovCqrfcyZWLe6Xf3XcWCmkImnWOs6gpkaEoTIlAUUoEClIiECWX9LsdBj86VIddZS2oazM69Ic/\nOQYLrd4AACAASURBVDkDUwbGOo0vTAlHeGgIkiNCkRwpQ3KkDCkRMsR1cVHg2AznD1f+gO8tJBRz\nhTwpsP8FIaI+Z7XZ8Kv1P6NJ+0tRFiYVozA5Au0GC2IVzl/n92VR3VnoHitvwYhRZpeFrt5sRdn5\nNY5PN+qwr6rVoVfaYLbBYDYjWi7ByAERKEyJwMiUcCRFyJweyx9mns1WG6w2m8uL9Q7VtOFwrQZa\nkwUaowWtejPq2o24cXiCy0K5vt2E4/VaAIBELEJCuBQJ4aEIk7r+b7SwIKlvXwwRURBjYU1exVkC\n/6EzWSASiZxWsBCLRBgar0RNmwGjUiNxeWokhiUqIXVjhYbecpx5HoBDnxzDqmsHoq7NiNLzhXRp\now5VrXpYe1gremFBIu4cleyz9Y1tNpvL5/73iUb8+0QjWnRmaIwWaI0WGCw23HV5sssid39VG9b9\nWOd0e0WL3uXzXjc0DpOzo5EQEYrYMGm325b3J3xvIaGYK+RJLKyJgkSzzoSyJh1ONejww9k2HK5t\nx0MT03B1rvPWzyumZPZJId1dm4XNZkO70YIWnRnNOjNadCbsON3iMPNcrzHhro+POj2uWNTRH52j\nCkOOSoGkiFD8eXcVGrS/rNIxPz/BK0X1sXMa7K9So0FrQqPGhIbza3PPyYvHbSOdC+UmrQmHax0v\n/hOL4PJCSgAYkRwOGwBlqBgKaUdfc+f25a6kd7HrIBEReR4La/Iq9rb5xocHa/G/+2scbhMBqGo1\nuBzfF0V1jdqA3/7jhL3P96/7qjEsQYk2Y0ch3aozw9TNtLO69CAicwohEYswKE6BHFXnZiEKZMTI\nnbbwHpEU3ue90u0GM86qDahqNSBOIUVBivOmNEfOabC2pNbp9gaN61VRrsyOwZAEJWLDJAgPlUAR\nKoZcIu7yQ8Co1EiMSo28tBcSBPjeQkIxV8iTWFgTBSibzYa6diPKmvQoa9KhrEmHbFUYbil0niUd\nECmzr8ecFSvHiORwFA2IRJTA4rOnC/x0JgtKG3U40aDFiXotTjRonYp2vdmKkmrH1ScUUjGiw6SI\nlksQEyaBMjQExeUt0J5fpSM2TIK/zBuCaAEbw/RVr/SBs214r6QGVa0Gh4v/puTEuCyshyUocXNB\nIuIUUsQpz/9RhCI6zPXvNiVShpRI17PNREQU2EQ2m62HTsXubdu2DUVFRX0VDxFdwGixuryg7UB1\nG5796rR9mbhO+UnheHV2rtN4i9UGsQi9ao24eMWNOKUUD09Ix1m1AScbOoroyhbnnmexCE63XTtY\nhWsGqxAdJkFMmNRp1rnz+Ty5SofRYsWpBh30ZguKBjjPBO+vUmPFF6UAAFmICAOi5EiNkqEwJQKz\nh8b1aSxEROTfSkpKMHXqVMHjOWNN5AdadCbsLGtBbZsRtW0G1LYZUdduRFqUHKuvH+Q0Pkomgc5k\nRbRcYt/IJDs2DANVrncvvJQL2D49Uu/Q99ygMeGpf5c6Pr6oY2OV3DgFBsUrMChOgdgwCR78xwmH\n3Qnvurzn3Qn7epUOncmCfZVqHDmnwdFzGpxq0MFktSE7Vo6/zHMurIfEK7Dq2oFIjZJBpZBC7KOL\nH4mIKPCwsCavCubeNpvNBrXB4rL9ot1owZu7q5xub9KZnG4DOjZl2XBbfp/P5tpsNtS0GXGoph2H\natpwqLYd59qdY1CFSTAqLfL/27vz+Kjrc1/gn9n3TCbJZLLvkIQQSAABEVAERFQEi1ppVapybLWe\nvvT21dPjPT3n9nZDbT1cevSe47Hnqq0ttp5WtFZxYTUqIhh2wpaV7JnJZPZ97h8JA5MZkhnIZP28\nXy9e8PvNb2a+g4/hyTfP73n6E+k0JYpSFFGn6l3LdMKRihWbx4+f7WoMHQvQ//dXnq6K2rlDLROj\nOkrJB41vU/lrC8WHsUKJxMSaKEF6nV6c6Xbg9EDN8eluB4LBIN68vzIimUtXSbG6NBUZGikyNLL+\n39VXrtMVCQVXlVRHm07YanHjaLsNR9ptONZuC3XWuEglEcIbCMIz0LUiTSXBf9xVNqbTCa1uH5rN\nLjT3utBsdqHJ7EKbxY2X15dH3HipV0mxrFiH7CQZZhhUKE9XXXFKIBER0bVgjTVRAnj9Aax97Sh8\ng4qMk2QivHx3OXQx3Iw30gbXSstEAigkQphd4WO+k2QiVGaoMSuz/1eBTgG7xz/q0wkvfmmKVhd+\nz+vH0OfyRZz/z/VlKNApEr42IiKaGlhjTTSKOqxupKmkEA+qYZaIhKgwqBAIAqUDNcel+v5+yyPR\nW3m4G/zsHj8u9PXv5raY3Wg2u3Cy0x7W5cLtD8Lt7y9NmZWpxqyBZDpfJ4+oKx6JnWf/wDcZ0eq9\n3zrehXqTE30uHywuPyxuH0wOL7asmY7ClMhEuVSvRK/Ti7xkedgvdtsgIqKxxMSaRtVkqG1z+QKo\naTDjgzNGHGm34Se3FGFhnjbiul/eHtmdYyQM3nl+vbYD91Smo9vuCSXSg8s5rmTDbAO+lYDphAda\n+nCi045GkwstfS6YnT7YPH48d1sJqqLUL+9vtqB2UCs+y/nDuLA0P2pi/bNVxSO6Xpr4JsPXFhod\njBVKJCbWRDGqNzqx/UQ39jX0hvosS0UCtFuiD1mJxVA7zy5fAN02D7rtHnTbvQN/9uJEpy2sS0ef\ny4fffNkW9roSkQA5STLkDuzk5ibLkKKQ4Nm9Tei5rEvH+qucThgMBtHj8EIhFkIti/wy8u6pHuxv\ntoSdE6B/Jz2adRV63FSsQ5JMBK1cjCS5GKe+smBxQeQ3LEREROMVa6yJYvTxWROe29sEAChPV+KW\n6am4sTA5amIZi16HF4+9VQeTs788Qy4WYqZBhV6XD102D6zu6EloNOV6JZYUJiM3WY7cZDkMamnU\nkour7RHdYnbhSLutfxBNrxNNvS5Y3X48tTgXq8siezt/fNaEZrMLhSlyFOgUSFVKoJKKrqntHxER\n0WhjjTXRNQoEg1F7Fy8uTEaz2YUVJSnI08nDHhsuYXV4/GgwOXHe5MR5oxP1JifO9Tjgv+zbWpcv\ngIOtl8ohxEIB9CoJ9Cop9OqB31X9CepLX7SidyAh16sk+Omq4oR26dhx2og3j3WFndPIRHD5AlGv\nXzEtJe73ICIimuiYWNOoGo+1bf5AEPUmJw63WXG4zYZ6kxP/755yKCThLdnkYiEevi4r4vmDa57f\nONKJTfOz0G71oN7oQL3JiTaLJ6a13Do9FbeXp0Kv6m+1d6XhJPNykkakS4fT68fZHgfquhw41WVH\naboS982OHIk+J1sDo8OL4lRF/0AanQIpSvGI12ZfbjzGCo1fjBeKFWOFEomJNU1pW2uasa/BHFF2\ncbjNhuvzY6vvfe90T1jNs9HhxbN7msKukQgFyNfJUZyqQFGKAsWpCqSpJPjB386FTSbcNH/4yYTA\ntXfpONFhw799dgGNvc6wseN9bl/UxHpuThLm5kROKSQiIqJLmFjTqBqrXYIrlXfYPX5Y3X4Y1FJU\nZalRlaVBVZYGqcqh+0zb3D581tSHPfW9OHTBGvF4jlaKhXnJoSQ6N1ke0ZIPuLbJhIMFgkH02L1o\ntbjRZnGj3eKGUCCIusuukolQb3JCODCKvEyvQlm6EjMMqqt+/5HGHSWKB+OFYsVYoURiYk2TUpfN\ng+Md/dMED7dZcc8sA+4oj7zJ7v45mXhoXlZM/aWdXj8+b+rD3nozDl6wwDuw1StAf3eQi5MJ9SoJ\n/s+a0hHfefb6A7C4/EhVRSb9HVY3HnnzVGhNFyXLxVET67xkOZ6/YxqmpSkhjzKKnIiIiOI37L/8\nLS0tePDBB9HV1QWBQIBHH30U3/ve90ZjbTQJJbq2bV99L176ojWsNAMATnTaoibWeclD34QoEwtx\noMWCPfW9ONDcB7f/UjI9O1ONG4t0WFyghVAgGNHJhG5fAC981gKjwwuTwwujw4c+lw8KiRBvb5wd\ncX2qUgJ/MIgUhRhZSTJkJcmQOfB7MBiM+KZBKBCgMkN9TWtMNNZBUjwYLxQrxgol0rD/+kskEmzZ\nsgVVVVWw2WyYO3cuVq5cifLy8tFYH1EEty8Ak8OLzChT9pRSEbrtXqikIsxIV6EyU4XqLA1KUpXD\nvu7gmxB/82UrBABcvku7wBUGFW4s0mFJYXJEuchwO89efwCtFjeael39v8wudFo9+Le10yMSX4lI\ngI/PmsK6hggFgEoigtsXgGzQLrNEJMT2jbO5+0xERDSGhk2sMzIykJHRfzOTWq1GeXk52tramFjT\nVbmaXQKLy4fjnTYc77DjRKcNZ3ucKEyR48V1ZRHXzsxQ46WvlUUdy30lTq8fdd0O/PfRzrCdbvdA\nQj09TYmbipKxtEiHdLU07vUD/QNV7v398agDUrrt3ojXFQoE+P7SfKhlIqQqJUhVSqCVi4fsAz3Z\nkmruKFE8GC8UK8YKJVJcP69ubGxEbW0tFixYkKj1EIVpMbvw7b/UwXdZ7fDF1NIfCEYkmnKxEIUp\niiH7SpudXpzotON4hw3HO+0R/aQvd391Bh6cmznkGq1uHw60WHCgxYJzPQ5sXl0SkSgLBAJkJ8lg\ndfuQr5MjX6dAfrIc+To5dIro/xuyFzQREdHEEnNibbPZcPfdd2Pr1q1Qq8d3bSaNX/HWtuVoZcjR\nypAkE2NmhgozM9QoT1dBJRVd8TmDSzq2HenAN6oMOG904XiHDS194SPIL3bGmJ6mRE2jGZaB1nt6\nlQTrhijv+PCMER+fM+FYuy0sMW/sdUbd2d5653ROHowD6yApHowXihVjhRIppsTa6/Vi/fr1uP/+\n+7Fu3bqIxx9//HHk5eUBALRaLSorK0NBW1NTAwA85jEA4NixYxGPBwJByAtnoShFgbraA2GPf/rp\np9igD2DZ0qWh62sbr/z6u/buw/t1RnSLCgAAlvOHYQHwgqMqdCwVCrBg0Q2YmaGGr/ko8pMVWLGs\nGgBQ7m3A4TYryqoXYFmxDkcP7r/i5znWYcO+T2ogFAA33LAYi/K1cDcdhb3eCuQujbheJBSM+d8/\nj3nMYx5P9eOLxst6eDy+ji/+ubm5GQCwadMmxEMQDAav8EPwfsFgEBs3bkRqaiq2bNkS8fjOnTsx\nZ86cuN6UCOhviffBGSN2nDai2+6NWnYxVEmHPxBES58LdV0O1HXbcbrbgQZT+MCTiwp0cqycloKZ\nGWqUpCogEQ1fj+z1B3C03QaJSIBZmZqIx09329FidmN+btI1dwEhIiKi8eerr77C8uXLY75+2Gzg\n008/xeuvv45Zs2ahurp/V2/z5s249dZbr36VNKXVG5145WAbvrxgCSXBmRppRH/maKPCH7kuE01m\nN+q67Djb44DDGwh7jlAA5CfL0WnzwOXrf0yvkuBXt08bNvkNBoNot3pwstOOL1r68GWLBQ5vANVZ\nmqiJdalehVL9+BmoQkRERGNr2MR68eLFCAQCw11GFJOamhrkVszFFy0WiIUCLCnU4rbSNMzOUkd0\n8dh5zhQxKvy5vc1h16SrJSjVq1CmV6IsXYWSVAUUEtGQO91XUm9y4rG3ToedK9DJMTODyfNYqKlh\nHSTFjvFCsWKsUCLx59eUMNEGkwBAvk6Bf7gxH/NyNEhWSCKec97oxMfnTHj/tDHiuTlaKZYU6FCa\nrkSpXnXF0eODJxp6/QE0mFyo67ajxezCdxflRjynQKdAhkaKAp0cszM1uD5fi6wovbKJiIiIohm2\nxno4rLGmwVy+AHafM+Htkz344U35KExRDPucbrsHu8714uNzJjT1ukLnRQKEOm7oVRL8+11lMdcz\nB4NBvHygDSc77ThrdMB7WeuO32+ogF51dT2piYiIaGoY8Rprolh1WN3468ke7DhjhHWgZd2OM0Y8\ntjAnammGw+NHTaMZH58z4UibDRfTXq1cjJuKkrG8JAWZSVLsOW8Oe97l/IEgms0uZGikUEjCW/AJ\nBAIcumBBw0CinqOVoSy9v2xEFsPNi0RERETxYGJNI2LXOROe29sUuhmxVK/E2hl6LC1KDrsJ0XL+\nMF6vmIvKDDW+bLHAPbCLLBEJsDBPixUlKbguNwniy/o9X17S0djrxKlOO84anTjX40C9yQmPP4hn\nVhdjTnZSxLoeui4LYqEApXolNDKG+0TCOkiKB+OFYsVYoURipkEjYnamBlKREDcUaLF2hh5l6Zdu\n+Hu/zhh2E2Kfy4+axj4AwMwMFVaUpGBpYTLUMSS+f6jtwJ56c9i5TI0UTm/0G2wX5mmv5uMQERER\nxY2JNcWs1+nF4TYbFuVrIROHl1KkqiT44zdnhpVjOL1+fNJgxp+Pd4XOJRX3D2qZn5uE7y7KQaYm\n/OZAfyCIE502yCUiTE9TRqxhXk4SBAIBSlIVKElToiRVwZ3oSYo7ShQPxgvFirFCicSMhK7I7vHj\naLsNtW1WHG6zonGgVvnZ1SWozo7s63wxqT7b48D7dUbsOm8K9ZkWAKEaar1Kgn+4MT9UL30xmd7X\nYEZNgxkmpw9LC5Pxo+WFEe9xy/RU3DI9deQ/LBEREdE1YmJNV/T8vqZQyQYAyEQCVGSoEe2+P7vH\nj10DLfLOGZ2h8+XpSqwuTcOcbDU+b7KgrvYLPHbXraGk+rzRgX/acR4mpy/0nAyNFPk6eeI+GE0I\nrIOkeDBeKFaMFUokJtZTmC8QxOluO+RiIYpTI8su5uYkodfpQ3WWBlVZapSlq+DyBrD7fC8aTC7c\nVJSMlj433j9txL763tCNiBqZCCtKUnBraWpYq721FXqk9iaHdfbI1sph9waQoZHixsJkLCnSYVqq\nImr/ayIiIqLxjH2spyCPL4AdZ4x440gneuxeLCvW4ellBcM+b/CI8ct7TANAVZYaq0tTcUN+MqSX\n1WDXG53Yfd6Eb1RnRLTEA4B2qxsZaimTaSIiIhpX2MearsjtC+C9uh788WgnTI7+0ousJOmQ0wWD\nwSA6bR4c67Dh3ZM9Yd09/EFAKRHizhl6rJqeimztpdcx2r3Ydd6EnedMqDf112bn6xRYMS0l4j0G\n38BIRERENBExsZ5CXL4AXjnYDpcvgAKdHGV6FYpS5Li55FKyGwwG0WJ242iHDcc6bDjeYQtLpgfb\nODcTd81MDzv3h9oO/Par9lBPa41MhKWFyShKUbC2jWLGWKF4MF4oVowVSiQm1lOIVi7G383PglIi\nwn992YodZ4wAgG1HOrG2Qo+z3Q4c77Sjz+ULe55GJsLMDDWmpSjw17oe9A7caKhXSbC8JHIHujBF\nAaFAgOvzkrB8Wgrm5yZBOnDHY1uCPyMRERHRWGGN9SRk9/hhdvrCSjMu9/aJbrz4+YUrPj9FKUZl\nhjr0K18nh3Cg/tni8mHXORN6HF6kqSRYV5Ee8XxfIAiHxx8xfpyIiIhoImGN9RRmc/uw/WQP3jre\nhbxkOf71jmlhNwQGg0EcarXi7ZNdEc8t0ytxe3kaKjPUyNRII5533ujAiU47TnXZcaLTjg6rB2Kh\nADcXp0Qk0GKhgEk1ERERTTnMfiYBs9OLd0724K0T3bB7/AAAgQBweANQSUXwBYLYW9+LN492od7U\n32N68MCWn60qvmIyLBAI8M8f1KPHcanWOkkmwo1FOngD8f3Ag7VtFCvGCsWD8UKxYqxQIjGxngT+\n8f1zoc4bszPVuL86A7OzNHB6/XjreBf+fLwLXbb+pDhFIca6mXrcWJiMAy1WBINBTEtT4tNGM052\n2XF3ZTrydYqI91hSlIw+pw8zDCrMSFehMEUBkZDt8YiIiIguYmI9QXh8AXj8Aahlkf/JFuUnI03l\nwNdnG1CZoUav04tXD7bhr6d6YHX372DnaGW4Z5YBy0t0kIqEAyUdNnx5wRra5QaAaWnKqIn1Ywtz\nRuRzcJeAYsVYoXgwXihWjBVKJCbW45jR7sUXLX34otmCr9qsWD9Tj2/Nywq7xuLyQSsX47qcJEiF\nwNaaZnx41gTvwOSWGekq3Ds7HQvztKEbEAFgX30v9tSbAQBpKgkq0lWYYVBhbrZm9D4gERER0STC\nxHocOtFpw4ufXcA5ozPsfLvVE3Y8eBLi5a7P1+LeynRUZKijvsedM/SQioS4tSx1VAe0sLaNYsVY\noXgwXihWjBVKJCbW41CSTIxzRidkYiHmZGmwIC8J83OTkKaS9g9w6XPj0AUL/lZnjEiqy/VKfP/G\nfORqZTjV5cCrB9sidrkBIDNJhoeuizxPRERERFeHfazHCYvLh93newEANxUlo97kQoVBBalYCLvH\nj9pWKw62WnDoghWdNs8VX+c7C7KRJBdj+4lunOlxAAB+fed0lKWrRuVzEBEREU0W7GM9Qdg9fmw7\n3IGV01KgU0jCSjr+dLQT31+ahzePdeHgBQtOddlxeVc7rVyMOdkaVBiU2Ha4C8aBNnhqqRDbjnSG\nJicmyUS4rSwNerV01D8fERER0VTDxHqU+QNBfHDGiFcPtsPs8qHe5MSCXG1YSUe33Yt/fP986Fgo\nAGYaVJiXk4R5OUkoSVOEbkS8qSgltNNtdvnw+9oOFKUosK5Cj2XFOsjEwtH9gMNgbRvFirFC8WC8\nUKwYK5RITKxH0ZE2K/59f2toSEuFQYVvzc3C7npTxLUaqQhLi5IxNycJ1VkaqKQitFvcqG2z4kyP\nA3eUpwEAkuRirK3QA+gvJ6nO0qAyQxU2OZGIiIiIEo811qPE6vbh/jdOwOkNIF0twabrslGcKser\nhzrwSYM57NoUhRgvrS+DTCzC501m1LbaUNt2qbY6TSXB7++rYPJMRERElECssR6nNDIxHpyTCZcv\ngJuLdXjzaBee2dOIQBCQigS4rSwNaUoxZGIRlhXrkCQXw+r24dk9TaH6ao1MhNmZalRlaRAIAiLm\n1URERETjBhPrUXRraSrePNqJR/9SB7cvAKEAWF2aiq/PTkemRhaxA62RibGmXA+9WoKqLA2KJ8EY\ncda2UawYKxQPxgvFirFCicTEeoT12D344IwJ36gyhBJljz+Av53qwR8OX+rYcX2+Fg9WZ+Bohw1P\n/fUs/nFZAaqzIqcefnfRyIwSJyIiIqLEYmI9gr5sseDZPY2wuP1o7nXhseuz8VWrFa8eakfHwNTE\nCoMK35qTgaY+N370YX2oVd7e+t6oifVkw10CihVjheLBeKFYMVYokZhYjwB/IIjfHmrHtiOdoXO7\n63vxSaMZvoEC6bxkOR65Lgt6lQT/66P6UHu9ohQFNs7NxMK8pDFZOxERERGNjPHV5HgCMju9+MF7\nZ7HtSCcGVz/7AkGoJEL8jyV5eOlrZbg+X4tsrQxefxAFOjn+ZXkh/u9dpbg+XztlOnzU1NSM9RJo\ngmCsUDwYLxQrxgolEnesr5FSIoLDE0CKUozSNBU+b+4Le/z+OZm4tTQ1dKyQiLBlzTRkJslCQ16I\niIiIaOIbdsf64YcfhsFgQGVl5WisZ8KRioX4n8vysTBPG5FUa2UirJyWEvGcbK18yibVrG2jWDFW\nKB6MF4oVY4USadjE+qGHHsKOHTtGYy0TUoPJiZ/vasR7dUaIBMAd5alIV0kAABq5GCqpaIxXSERE\nRESjYdjEesmSJdDpdKOxlnHvcJsVdo8fABAMBrH9RDeeePs0GnpdyNJIsWaGHh+cMaHL7kWaUoJ7\nKtPHeMXjD2vbKFaMFYoH44VixVihRGKNdQz8gSB+91U7th3uxNLCZHxnYTae/6QZBy9YAfQPeTE7\nvdh+ohsAsGp6Cr6zMIe71URERERTCBPrYRgdXmze1YijHTYIBYBIJMC3/1IHi9sPjUyEpxbnYXFh\nMj5v6sOZHieeWpKL+bnasV72uMXaNooVY4XiwXihWDFWKJFGJLF+/PHHkZeXBwDQarWorKwMBe7F\nH7lMxOMvWyx4+jfbYfP4kT9zHsr0Smz/YDcA4MYli/GDG/NRV3sANa3911dna3Bw/2eoaRof6+cx\nj3nMYx7zmMc85nHsxxf/3NzcDADYtGkT4iEIBoPB4S5qbGzEmjVrcOzYsYjHdu7ciTlz5sT1phOB\nxeXDT3c24Ei7DSWpCji9AbRa3BAJgIfmZeLuWYYp29njWtTU1ISCmGgojBWKB+OFYsVYoXh89dVX\nWL58eczXD3vz4oYNG7Bo0SKcOXMGubm5eOWVV65pgROBxeXDY2/V4Ui7DQBwzuhEq8WNLI0UJWlK\neAJgUk1EREREYWLasR7KZNyxfvtEN178/ELYuSyNFL1OH5y+AFKVErxy7wzIxRxcSURERDRZxbtj\nLU7gWiaEYDCIPfVm6FUSzMxQw+0LYP+gQS8A0Gb1AAAWF2jx9zfkMqkmIiIiojBTOjs02r348ccN\n2Ly7Eb/a14z9TWZ8+y+ncKjVGnGtWirC08sK8M/LC6FTSEZ/sZPE5TcHEA2FsULxYLxQrBgrlEhT\ncsc6GAzio7Mm/Mf+Vtg8fijEQmhkIvzLRw0AgAKdHJuuy0K71QOr24d6kxNPLMpFipIJNRERERFF\nNyVrrJ/b24SPz5oA9CfRXTYPHN4AZCIB7p+TifWV6RALeXMiERER0VTGGusYLMrT4rNGM5LlYjT2\nugAAOVoZnrg+B3NyksZ4dUREREQ0EU25GmuXL4DTPQ64vAG0WT3QSIVIU0lwoc+NN4914Ro38GkY\nrG2jWDFWKB6MF4oVY4USaVLvWBsdXvgDQXze1N/lI1kuxn8dbEPHQIePTI20v47aE0BWkhRrK/QQ\nsD81EREREV2FSVlj7fD48eaxLvz30U6IhAI4vIGwxwt0MnTbfbB7/JCJBNhQlYG7K9MhZQs9IiIi\nIhowpWusvf4A3qsz4vXaDvS5fP0n/eHfNyzK1+JHywvxpyOdqDc58eiCbKSrpWOwWiIiIiKaTCbN\nFq3F5cPf/fkUXvz8AvpcPqQpJRBFqeqoztJALBRgQ5UBP1peyKR6lLG2jWLFWKF4MF4oVowVSqRJ\nk1gnycXI0MiQJBNBKhKgx+GFPwhc3jVPr5JgWbEOAFhLTUREREQjalKUgji9frxzsgenu+2w+HUA\nxgAACSpJREFUe/rrqZPlYphdPgSCwO1lqSjQKbCsWIck+aT4yBPW4sWLx3oJNEEwVigejBeKFWOF\nEmnCZZldNg9OdtpxU7EOHl8Af6vrwbbDnTAP1FRrZCJY3X6YXT5IRQKsLk3D12enI03Fkg8iIiIi\nSpwJk1h32Tz410+acaTNCoFAgDaLG++e6kGPwwsAKNUrUaZX4u2TPZCJBLijPA13zzIglWPIx5Wa\nmhruFlBMGCsUD8YLxYqxQok07hPrYDCIHaeN2PppCwLB0Em8eqgdAFCUosDGuZlYmJcEbyCIJLkY\nd5SnQadgQk1EREREo2fc97H+7aF2vF7bEXFeKxPh72/IxeLCZAh5IyIRERERjbB4+1iP+64gFQZV\n1LZ5c7I1WFqkY1JNREREROPCuE2s+1w+vPBZC/7pg/ODZ7xAAKAsXTUm66Jrw/6hFCvGCsWD8UKx\nYqxQIo2bGuuzPQ6opCKkqSR4+0Q3/nC4E3aPH5fvR0tFAszJ1uC71+fAoJGN2VqJiIiIiAYb8xpr\ni8uHVw624b06I4pSFXB4fGi39nf6mJutwab5Wdha04KZGWrcN9vAPtRERERENCrirbEesyzVHwji\n/dNGvHKwDVa3HwBw3ugEAOQly/HogizMz9UCALasmQ6RkLXURERERDR+jXqNtcXlw/bjXXj4zZP4\n9actoaQaABRiIZ5YlIOXvlYWSqoBMKmeRFjbRrFirFA8GC8UK8YKJdKo7lhbXD489lYduu3eqI+L\nhAKsKElhIk1EREREE86oJta7z/deMaku1MnxyPwsKCTjtlEJjQBOu6JYMVYoHowXihVjhRJpVBNr\nbyDyPkmNTITHFuZgWbGOO9VERERENGGN2vaw2xfA5019YedUUiH+82tlWDGN5R9TBWvbKFaMFYoH\n44VixVihRBqVHWuPL4Aff1SPYx02aOUi3F6aBp1SgmXFOrbPIyIiIqJJIeF9rD3+AP73Rw348oIF\nWrkYv7q9BPk6xbW8JRERERFRwsXbxzqhpSAefwA//fhSUv3cbUyqiYiIiGhySlhi7fUH8LOdDfii\nxYIkmQjPri5BYQqT6qmOtW0UK8YKxYPxQrFirFAiJSSx9gWC+PmuRuxvtkAjE+HZ20pQlMqkmoiI\niIgmrxGvsfYFgvjFrgbUNPZBLe1PqqelKa95oUREREREo2lMa6z9gSCe2d2ImsY+qKQiPMOkmoiI\niIimiGET6x07dqCsrAzTpk3Ds88+e8Xr/IEgnt3TiH0NZiglQjyzuhjTmVTTIKxto1gxVigejBeK\nFWOFEmnIxNrv9+OJJ57Ajh07cPLkSWzbtg2nTp2KvC4QxHN7m7Cnvj+p3ry6BKV6VcIWTRPXsWPH\nxnoJNEEwVigejBeKFWOFEmnIxPrAgQMoKSlBQUEBJBIJ7rvvPrz99tsR123e3YDd53uhkAjx81uL\nUZ7OpJqi6+vrG/4iIjBWKD6MF4oVY4USacjEurW1Fbm5uaHjnJwctLa2Rly3r6EPMpEAP19VjAqD\neuRXSUREREQ0zg2ZWAsEgphfaHVZGmZmMKmmoTU3N4/1EmiCYKxQPBgvFCvGCiXSkO329u/fjx//\n+MfYsWMHAGDz5s0QCoX44Q9/GLrmxdfeQFmOPvErJSIiIiIaRTabDWvXro35+iETa5/Ph9LSUuzc\nuRNZWVmYP38+tm3bhvLy8hFZLBERERHRZCEe8kGxGC+88AJWrVoFv9+PRx55hEk1EREREVEU1zx5\nkYiIiIiIrnHyYqzDY2hqevjhh2EwGFBZWRk6ZzKZsHLlSkyfPh233HILzGbzGK6QxouWlhYsW7YM\nFRUVmDlzJn79618DYLxQJJfLhQULFqCqqgozZszA008/DYCxQkPz+/2orq7GmjVrADBeKLqCggLM\nmjUL1dXVmD9/PoD4Y+WqE+tYh8fQ1PXQQw+Fbny96JlnnsHKlStx5swZLF++HM8888wYrY7GE4lE\ngi1btuDEiRPYv38/XnzxRZw6dYrxQhHkcjl2796Nw4cP4+jRo9i9ezdqamoYKzSkrVu3YsaMGaFu\nZ4wXikYgEGDPnj2ora3FgQMHAMQfK1edWMc6PIamriVLlkCn04Wde+edd7Bx40YAwMaNG7F9+/ax\nWBqNMxkZGaiqqgIAqNVqlJeXo7W1lfFCUSmVSgCAx+OB3++HTqdjrNAVXbhwAe+99x42bdqEi9Wv\njBe6ksEV0vHGylUn1rEOjyG6XGdnJwwGAwDAYDCgs7NzjFdE401jYyNqa2uxYMECxgtFFQgEUFVV\nBYPBECohYqzQlTz11FP45S9/CaHwUsrDeKFoBAIBVqxYgXnz5uHll18GEH+sDNkVZLg3J7oWAoGA\ncURhbDYb1q9fj61bt0Kj0YQ9xnihi4RCIQ4fPoy+vj6sWrUKu3fvDnucsUIXvfvuu0hPT0d1dTX2\n7NkT9RrGC1306aefIjMzE93d3Vi5ciXKysrCHo8lVq56xzo7OxstLS2h45aWFuTk5Fzty9EUYTAY\n0NHRAQBob29Henr6GK+Ixguv14v169fjgQcewLp16wAwXmhoWq0Wt99+Ow4dOsRYoag+++wzvPPO\nOygsLMSGDRuwa9cuPPDAA4wXiiozMxMAoNfrcdddd+HAgQNxx8pVJ9bz5s3D2bNn0djYCI/Hgz/+\n8Y+48847r/blaIq488478dprrwEAXnvttVACRVNbMBjEI488ghkzZuDJJ58MnWe80GA9PT2hu/Kd\nTic++ugjVFdXM1Yoql/84hdoaWlBQ0MD3njjDdx888343e9+x3ihCA6HA1arFQBgt9vx4YcforKy\nMu5YuaY+1u+//z6efPLJ0PCYi22PiABgw4YN2Lt3L3p6emAwGPCTn/wEa9euxb333ovm5mYUFBTg\nT3/6E5KTk8d6qTTGampqsHTpUsyaNSv0Y7bNmzdj/vz5jBcKc+zYMWzcuBGBQACBQAAPPPAAfvCD\nH8BkMjFWaEh79+7F888/j3feeYfxQhEaGhpw1113AeifPP7Nb34TTz/9dNyxwgExREREREQj4JoG\nxBARERERUT8m1kREREREI4CJNRERERHRCGBiTUREREQ0AphYExERERGNACbWREREREQjgIk1ERER\nEdEIYGJNRERERDQC/j/XYpe3IUOceAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x114909550>"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The cumulative hazard has less immediate understanding than the survival curve, but the hazard curve is the basis of more advanced techniques in survival anaylsis. Recall that we are estimating $\\Lambda(t)$, the cumulative hazard curve. (Why? The sum of estimates is much more stable than the individual estimates.) Thus the *rate of change* of this curve is the hazard function.\n",
      "\n",
      "Looking at the hazard function of regimes, it looks like the hazard starts off high and gets smaller (as seen by the decreasing rate of change). Let's break the regimes down between democratic and non-democratic:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "naf.fit(T[dem], censorship=C[dem], columns=[\"Democratic Regimes\"])\n",
      "ax = naf.plot()\n",
      "naf.fit(T[~dem], censorship=C[~dem], columns=[\"Non-democratic Regimes\"])\n",
      "naf.plot(ax=ax, c=\"#A60628\")\n",
      "plt.title(\"Hazard function of different global regimes\");"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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MmTMcP34nRWnDhg3q+9WgQQM0Gg0GBtL0RfW7XVzKp3tT2HjyIut/y6ntcIQQ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WLjc2PtjMsyS5MDIQDP3nreO/3KhOOGnCzO3vDSEf0/OeLyd4uvj1fV0NvTKfnOjUTcHgItHQRa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\nJAH3ICmKw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mg0SjQaZenSpdxxxx10dXXJfBEDeuedd3j44Yd55ZVXZK6IpKqr\nq7nsssuAWKf1a665hjvvvHPI80Ua3wghhBBCCDGCRqTxjRBCCCGEECJGAm4hhBBCCCFGkATcQggh\nhBBCjCAJuIUQQgghhBhBEnALIYQQQggxgiTgFkIIIYQQYgRJwC2EEEIIIcQIkoBbCCGEEEKIEfT/\nAeHZyP9w/xetAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x114914d10>"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Looking at the rates of change, I would say that both political philosophies have a constant hazard, albeit democratic regimes have a much *higher* constant hazard. So why did the combination of both regimes have a *decreasing* hazard? This is the effect of *frailty*, a topic we will discuss later. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}